Automated Medical Diagnosis By Ranking Clusters Across The Symptom-Disease Network

The rapid growth of medical recording data has increased the demand for automated analysis. An important problem in recent medical research is automated medical diagnosis, which is to infer likely diseases for the observed symptoms. Existing approaches typically perform the inference on a sparse bipartite graph with two sets of nodes representing diseases and symptoms, respectively. By using this graph, existing methods basically assume no direct dependency exists between diseases (or symptoms), which may not be true in practice. To address this limitation, we propose to integrate two domain networks encoding similarities between diseases and those between symptoms to avoid information loss as well as to alleviate the sparsity problem of the bipartite graph. Another limitation of the existing methods is that they usually output a ranked list of diseases mixed from very different etiologies which greatly limits their practical usefulness. An ideal method should allow a clustered structure in the disease ranking list so that both similar and different diseases can be easily identified. Therefore, we formulate automated diagnosis as a novel cross-domain cluster ranking problem, which identifies and ranks the disease clusters simultaneously in the symptom-disease network. Our formulation employs a joint learning scheme in which the dual procedures of cluster finding and cluster ranking are coupled and mutually reinforced. Experimental results on real-world datasets demonstrate the effectiveness of our method.